Detection of surging sound with wavelet transform and neural networks

被引：0

作者：

Kotani, M ^{[1
]}

Akazawa, K ^{[1
]}

Kanagawa, T ^{[1
]}

机构：

[1] Kobe Univ, Fac Engn, Kobe, Hyogo 6578501, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 1998年 / E81D卷 / 03期

关键词：

acoustic diagnosis; surging sound; blower; neural networks; wavelet transform;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An acoustic diagnosis technique for the blower by wavelet transform and neural networks is described. It is important for this diagnosis to detect surging phenomena, which lead to the destruction of the blower. Dyadic wavelet transform is used as the pre-processing method. A multi-layered neural network is used as the discrimination method. Experiment is per formed for a blower. The results show that the neural network with wavelet transform can detect surging sound well.

引用

页码：329 / 335

页数：7

共 14 条

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